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Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. ABB AB, Sweden. (Corporate Research)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2009 (English)In: Proceedings of the 41st ISCIE International Symposium on Stochastic Systems, Institute of Systems, Control and Information Engineers , 2009, 1-6 p.Conference paper, Published paper (Refereed)
Abstract [en]

In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.

Place, publisher, year, edition, pages
Institute of Systems, Control and Information Engineers , 2009. 1-6 p.
Keyword [en]
Grey-box, Identification, Estimation, Nonlinear, Optimization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-95544ISBN: 9784915740473 (print)OAI: oai:DiVA.org:liu-95544DiVA: diva2:635905
Conference
41st ISCIE International Symposium on Stochastic Systems, Kobe, Japan, 13-14 November, 2009
Available from: 2013-07-07 Created: 2013-07-07 Last updated: 2013-12-04

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Isaksson, AlfTörnqvist, DavidSjöberg, JohanLjung, Lennart

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